3D Dense Volumetric Network for Accurate Automated Pancreas Segmentation

Pancreatic cancer poses a great threat to our health with an overall five-year survival rate of 8%. Automatic and accurate segmentation of pancreas plays an important and prerequisite role in computer-assisted diagnosis and treatment. Due to the ambiguous pancreas borders and intertwined surrounding tissues, it is a challenging task. In this paper, we propose a novel 3D Dense Volumetric Network (3D 2 VNet) to improve the segmentation accuracy of pancreas organ. Firstly, 3D fully convolutional architecture is applied to effectively incorporate the 3D pancreas and geometric cues for volume-to-volume segmentation. Then, dense connectivity is introduced to preserve the maximum information flow between layers and reduce the overfitting on limited training data. In addition, a auxiliary side path is constructed to help the gradient propagation to stabilize the training process. Adequate experiments are conducted on a challenging pancreas dataset in Medical Segmentation Decathlon challenge. The results demonstrate our method can outperform other comparison methods on the task of automated pancreas segmentation using limited data.Clinical relevance - This paper proposes an accurate automated pancreas segmentation method, which can provide assistance to clinicians in the diagnosis and treatment of pancreatic cancer.